Coiling Temperature Prediction and Application Based on Genetic-Neural Network on Hot Strip Mill

Tie Jun Sun; Wei Dong Yang; Hai Gao; Hong Tao Mi

doi:10.4028/www.scientific.net/AMM.448-453.3417

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 448-453Coiling Temperature Prediction and Application...

Coiling Temperature Prediction and Application Based on Genetic-Neural Network on Hot Strip Mill

Abstract:

Coiling temperature control (CTC) is very important to the quality of the strip steel in Hot Strip Rolling Mill. In the paper, genetic algorithm and neural network method to predict coiling temperature on hot strip mill were put forward. The genetic-neural network was trained and checked with actual production data. The result indicates that the method can real-time predict the strip coiling temperature. The on-line prediction model and step track method has been put into use. The result shows that the method can settle lag influence in feedback control and the CTC control precision is improved greatly.

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Periodical:

Applied Mechanics and Materials (Volumes 448-453)

Pages:

3417-3420

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.448-453.3417

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Online since:

October 2013

Authors:

Tie Jun Sun*, Wei Dong Yang, Hai Gao, Hong Tao Mi

Keywords:

Coiling Temperature Control, Genetic-Neural Network, Iterative Algorithm, Nonlinear, Training Set

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* - Corresponding Author

References

[1] Prieto M M, Ruiz L S, Menendez J A . Thermal performance of numerical model of hot strip mill runout table[J] . Icon making and Steeling, 2001, 28(6): 474-480.

DOI: 10.1179/irs.2001.28.6.474

Google Scholar

[2] Van Ditzhuijzen G A J M, Control of runout table cooling in a hot strip mill: present possibilities and future development Steel Timers Int. Jan (1994).

Google Scholar

[3] WU Jian-yu, HE Xiao-rong . DOBD Algorithm for Training Neural Network Park I Method[J] . The Chinese Journal of Process Engineering, 2002, 2(2): 171-176.

Google Scholar

[4] D.J. Janson J.F. Frenzel . Training Product Unit Neural Networks with genetic Algorithms[J] . IEEE Expert, 1993, 10, 8(5): 26-33.

DOI: 10.1109/64.236478

Google Scholar

[5] Y.Y. Yang D.A. Linkens,J. Talamantes-Silva . Roll load prediction-data collection, analysis and neural network modeling[J] . Journal of Materials Processing Technology, 2004, 152: 304-315.

DOI: 10.1016/j.jmatprotec.2004.04.372

Google Scholar

[6] Zhi-Hua Zhou . Three perspectives of data mining[J] . Artifical Intelligence, 2003, 143: 139-146.

Google Scholar

[7] Information on http: /www. chianisa. org. cn.

Google Scholar